Returns
List of Documents selected by maximal marginal relevance.
similarity_search(query: str, k: int = 4, filter: Optional[Dict[str, Union[str, int, bool]]] = None, **kwargs: Any) → List[langchain.docstore.document.Document][source]#
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query.
similarity_search_with_score(query: str, k: int = 4, filter: Optional[Dict[str, Union[str, int, bool]]] = None) → List[Tuple[langchain.docstore.document.Document, float]][source]#
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query and score for each
class langchain.vectorstores.VectorStore[source]#
Interface for vector stores.
add_documents(documents: List[langchain.docstore.document.Document], **kwargs: Any) → List[str][source]#
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
abstract add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]#
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.